Review ArticleA Review of Underwater Localization Techniques, Algorithms,and Challenges

Xin Su ,1 Inam Ullah ,1 Xiaofeng Liu,1 and Dongmin Choi 2

1The College of Internet of Things (IoT) Engineering, Hohai University (HHU), Changzhou 213022, China2The Division of Undeclared Majors, Chosun University, Gwangju 61452, Republic of Korea

Correspondence should be addressed to Dongmin Choi; jdmcc@chosun.ac.kr

Received 14 October 2019; Revised 26 November 2019; Accepted 3 December 2019; Published 13 January 2020

Guest Editor: Yuan Li

Copyright © 2020 Xin Su et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source forthe exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN isbeing developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, areview has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, anintroduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paperfocuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization.Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are furtherclassified into estimated and prediction-based localizations. Also, various underwater localization algorithms are discussed,which are grouped by the algorithms based on range and range-free schemes. Finally, the paper focuses on the challengesexisting in underwater localizations, underwater acoustic communications with conclusions.

1. Introduction

Underwater wireless sensor networks (UWSNs) have shownincreasing interest, in the latest years. For a variety of appli-cations, underwater sensor networks (USNs) can be imple-mented. Each implementation is essential in its domain,but some of them can enhance ocean exploration to meetthe variety of underwater applications, including a naturaldisaster alert scheme (i.e., tsunami and seismic tracking),aided navigation, oceanographic information collection,and underwater surveillance, ecological applications (i.e.,quality of biological water, tracking of pollution), industrialapplications (i.e., marine exploration), etc. For example, foroffshore engineering applications, sensors can assess certainparameters such as base intensity and mooring tension tomonitor the structural quality of the mooring environment[1]. Underwater acoustic sensors networks (UASNs) providea new platform for under communication to explore theunderwater environment. UASNs have also improved theunderstanding related to underwater environments such as

climate changes, animal life in underwater, and the popula-tion of coral reefs.

In [2], the authors present a localization technique forUASN in which the mobility of the sensor node is consideredand all the unknown sensor nodes are successfully placed atdifferent positions. The positioning system is recursive andthe localization method involves distinct sensor nodes.UASNs also increase the underwater warfare capabilities ofthe naval forces so that they can be used for the detectionof a submarine, unmanned operation system, surveillance,and mine countermeasure algorithms. UASNs can also helpmonitor or control the oil rigs that can take prevent the disas-ter’s effects such as rigs explosion in the Gulf of Mexico onceoccurred (2010). Similarly, UASN technology also helps intsunami and earthquake forewarning. A unique system iscalled 3-DUL, which originally consisted of only threeanchor sensor nodes, such as buoys on the water surface,which defuse their worldwide position data in all three direc-tions and 3-DUL follows a 2-phase operation [3–6]. The dis-tances to nearby anchor nodes are determined by a node with

HindawiJournal of SensorsVolume 2020, Article ID 6403161, 24 pageshttps://doi.org/10.1155/2020/6403161

an unknown place during the first stage. The anchor nodesare projected to their horizontal level in the second phaseand form a virtual geometric shape using the depth informa-tion from these multivariate ranges. If the correspondingshape is robust, the sensor node will find itself and becomean anchor sensor node through the dynamic trilaterationmethod. In three-dimensional (3D) topology, this methoditerates dynamically in all directions to locate as many sen-sors as possible. A 3D localization method takes into accountthe attenuation of electromagnetic (EM) waves over the reli-able elevation angle spectrum. They pick the radiation pat-terns of dipole antennas to determine the reliable elevationrange. The feasibleness of this scheme is presented in dis-tance estimation and 3D localization schemes by changingthe elevation angle and distance. However, in [7], the writerssuggest a fresh model that utilizes the benefits of the featuresof EM waves in water. The sensor node cannot only evaluatethe distance with low environmental noise but also ensureprecise localization output with elevated sampling rates.Using the sets of RF sensors, a UWSN is built for this locali-zation system at the target docking location. A 3D underwa-ter localization algorithm is also suggested in [8] for a marinenear-sea surveillance scheme that utilizes a tiny amount ofbeacons for localization. Performance evaluations show thatthe worldwide localization of three surface anchor nodes iseffectively spread by 3DUL. Its simple algorithm makes itpossible for UASNs to adapt to the vibrant nature of thewater globe [9, 10]. To this end, the ocean surveillance systemis used that can gather information from the ocean and itssurrounding areas and provide this information via satellitecommunication to the ship or on-shore center or sometimesuse underwater wires. These are replaced by less expensiveand small underwater nodes that use this equipment inUASN to house various nodes on board, such as pressure,temperature, and salinity. Underwater sensor nodes are net-worked and can interact using acoustic signals.

As we know, in underwater the radio signals can onlytravel to a short distance because the radio signal attenuateshighly underwater and optical signals cannot travel in aninappropriate medium because of the dispersion of the opti-cal signals. An acoustic signal scatters less as compared toradio and optical signals, resulting in an acoustic signal beingmore useful for underwater communication purposes ascompared to radio and optical signals as shown in Table 1.However, the acoustic bandwidth in the underwater issmaller, resulting in reduced information rates. Multiple sen-sor nodes are needed to raise information rates and have

short-range communication, resulting in excellent coverage.The acoustic channel also has a low quality of connection[11, 12] owing to the time variability of the propagationof the medium and multipath. The underwater sound speedis approximately 1500m/s, resulting in very elevated delaypropagation. The UASNs are also energy-limited as WSNdue to these difficulties. Also, localization is a fundamentaltask that is used to detect the location of a target in the under-water medium for various purposes such as data tagging,tracking nodes in the underwater and coordinating themovement of node groups. For the tracking of a target [6,13, 14], research work proposes a semidefinite program-ming- (SDP-) based localization procedure that is achievedby measurements obtained via onboard pressure sensors.SDP enhances the point localization precision and providesquicker convergence for monitoring under the same systemsetup and environmental circumstances, particularly at lowsignal to noise ratio (SNR). On condition that geomagneticanomaly can be reversed as a magnetic dipole target, thelocalization of an underwater vessel relative to the target iscalculated by the magnitude of the magnetic field and targetgradients. The magnetic field is calculated by the devicemounted on a car comprised of ten magnetometers of oneaxis. Since the noise of magnetometers results in the coeffi-cients of a six-order formula [15] with an unsuitable element,the localization accuracy will be influenced by the weather. In[16], USNs can modify ocean exploration to enable a list ofnew applications that are not presently feasible or expensiveto implement, including oceanographic information collec-tion, ecological applications, government security, underwa-ter military tracking, and commercial operation.

For maritime defense purposes, USNs can provide imme-diate deployment and enhance coverage in coastal area sur-veillance applications. USNs mounted on the bottom of theocean with underwater sensor nodes can detect earthquakesand tsunami formations before entering residential areas. Arough drawing of underwater node operation is shown inFigure 1. Mobile USNs can track polluted waters for waterpollution detection devices as they propagate to clean waterfrom their source and warn authorities to take action. USNscould be used to monitor coastal creatures and coral reefs,where there is limited data about human activity. The Gauss-ian noise injection detector (GNID) is proposed [17], toimprove the probability of detection based on the noise-enhanced signal detection using a prewhitening filter, timefrequency denoising technique with S-transform, in inversewhitening filter results in improving underwater signal

Table 1: Comparison table of electromagnetic, acoustic, and optical waves in underwater environment.

Electromagnetic waves Acoustic waves Optical waves

Frequency band ∼kHz ∼MHz ∼1014-1015 Hz

Bandwidth ∼kHz ∼MHz ∼10-150MHz

Power loss >0.1 dB/m/Hz ∼28 dB/1 km/100MHz ∝ turbidity

Effective range ∼1m ∼10m ∼10-100mNominal speed (m/s) ∼1,500 ∼33,333,333 ∼33,333,333Antenna size ∼0.1m ∼0.5m ∼0.1m

2 Journal of Sensors

detection. Environmental monitoring is also a vibrant aspectof determining safety and health problems for environmentalor mankind’s health. Environmental monitoring’s main pur-pose is sampling soil, water, and atmospheric but they alsoneed to take the air samples inside buildings to guaranteerules are met. The group of people working in environmentalmonitoring needs to looking for many things which areimportant in fact. The most obvious is the radioactivity orpollutants, especially in the case when looking to build a caseof negligence against a distinct or for evidence of the effects ofchanges in climate.

The key objectives of this paper are to outlines a compre-hensive review of underwater localization techniques andtheir algorithms. We attempt to review the different aspectsof underwater communications and underwater localizationsby considering various attributes. The main objective of thisreview is to provide a detailed knowledge of underwaterlocalization techniques, localization algorithms, architecture,etc. We also highlight the weaknesses and strengths of theexisting underwater localization techniques that can helpthe researchers to identify more efficient and accurate solu-tions for the existing challenges. Explicitly, this review aimsto answer the following basic questions:

(i) What are the state-of-the-art localization techniques?

(ii) What are the main research challenges in underwa-ter localization?

(iii) What are the main defenses and their pros and cons?

(iv) What are the promising solutions to improve under-water localization?

This review makes the following contributions:

(i) This review provides a detail explanation of localiza-tion for the underwater environment which coversthe localization basics, architecture, localizationtechniques, and algorithms used for localization

(ii) This review rises the basic requirements for localiza-tion such as security attacks on underwater nodes

(iii) Based on a detailed analysis of existing underwaterlocalization techniques, we presented the existingchallenges and future directions that need to be con-sidered in the recent future

The paper is organized as follows. Section 2 presents theprocedure and basics of localization. Next, Section 3 presentsthe architecture of UASN, Section 4 presents the relatedworks, and Section 5 presents the techniques used for UASNlocalizations. Furthermore, Section 6 and Section 7 presentrange-based and range-free algorithms for localization,respectively. Section 8 presents the performance evaluationof underwater localization schemes. Finally, Section 9 pre-sents the existing challenges and open issues in UWSNsand Section 10 concludes the paper.

Watersurface

Reference node

Surface buoys

Ordinary node

Figure 1: A rough sketch of the underwater node deployment.

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2. Procedure and Basics of Localization

Assumptions for the localization operation need to takecare of is that all antenna nodes have an ideal understand-ing of their position and should share clock informationwith the other sensor nodes worldwide synchronization.All nodes should meet to share data at any time, i.e., everyindividual sensor node can retrieve all readings and exe-cute the process of localization before carrying all databack to the active or reference nodes. Each sensor nodein the corresponding destination frame can communicatecompletely with other nodes and has no accident or inter-ference problems [18]. At time t0, the active sensor nodeemits a single message requesting location through all ofthe listening nodes and each node gets message at the timetn,n. On the given message, each node conducts a Dopplerspeed estimate. After gathering all data from sensors, amaster node gets all estimates and performs the operationof localization and transmits the complete estimate back tothe active node. Alternatively, it is possible to collect andrelay the data to the active node where localization is doneafter that. The updates of the active node or master nodeestimate tracking and navigation algorithms as estimates ofpoints are acquired. Localization is another difficult work;the use of Global Positioning System (GPS) is limited to sur-face nodes because in underwater, GPS signal does not prop-agate [19]. Alternative GPS fewer positioning approaches forterrestrial sensor networks have been provided, but theymust be amended owing to the description of the acousticchannel. The acoustic channel has low bandwidth, highdelay in propagation, and high error rate. Localizationprotocols, therefore, need to operate with the minimal fea-sible exchange of messages between nodes and exchange ofmessages such as two-way data exchange (see in Figure 2).This is also assessed by the sensor node’s restricted battery

energy and the underwater sensor node battery rechargeor replacement problem.

To execute localization in a better way, it requires severalobjects with known locations, i.e., anchors and distance orangle measurement between anchors and the object to belocated underwater, i.e., unknown sensor node. The anchorscan be placed in a fixed position and their coordinates mayhave been configured in the beginning, or they may have dis-tinctive hardware to learn from the location server such asGPS. Using angle or distance measurement between theanchor and the unknown node to estimate the location ofan unknown sensor node and also combining measurementsoccur. Sensors presently used for oceanographic studies areeither located with long or short baseline (LBL/SBL) devices.With a set of receivers based on acoustic wave communica-tion, sensor positions are described in both instances. Acous-tic transponders are introduced in the LBL either on theseabed or underfloor moorings around the application region[20, 21]. The effects of multiple error sources on the LBL-based scheme’s localization accuracy have been investigatedand evaluated in detail. It shows that the more severe vari-ables that may affect general accuracy are the calibration ofthe transmitters and the amounts of data about the soundspeed in the operating area. A vessel follows the sensors inthe SBL scheme and utilizes a short-range emitter to allowthe process of localization.

UWSN is characteristically composed of various nodesthat are anchored to the lowermost of the ocean wirelesslylinked with underwater gateways [22–24]. The informationfrom these sensors is transmitted within this network fromthe lowermost of the sea to the water surface station by apply-ing multihop links. The gateways in underwater are furn-ished with definite nodes with both upright and straighttransceivers. The first gateway is utilized to transfer instruc-tions and constellation information to the nodes and receive

Ordinarynode

Referencenode. A

Referencenode. B

Referencenode. C

Referencenode. D

Req

Req

ReqReq Resp

Resp

Resp

Resp

Figure 2: Two-way message exchange of reference and ordinary node.

4 Journal of Sensors

the collected information back from the node. The secondgateway is used to transmit the supervised information tothe water surface station. Contrasting shallow water, uprightcommunication is typically essential for a long range in deepwater to attain the transfer of information to the watersurface station. The acoustic communication is applied toaccomplish multiple communications to collect the informa-tion from the nodes, where radio communication is generallyconventional with satellite communication to transmit thecollected information to the coastal sink. The underwatersensor nodes in the propagation range will sense a series oftransmissions and decode packets. To compare the receivingtime with the transmission time encoded in the packet, everysensor node can achieve the time of arrival (ToA) estimatesof the packet message from various surface nodes, basedon which it tries to calculate its particular location. Thebroadcast from the surface nodes to underwater nodes isone-way communication and the quality of localization isindependent of the number of sensor nodes in underwater,and there is no extra interference between underwater sen-sor nodes. The underwater sensor nodes contain a controllerto accommodate with an oceanographic sensor through asensor pledge micro security [25]. The receiving data fromthe sensor through the controller is stored in the onboardmemory. The controller can store, progress, and broadcastto the network devices.

3. Architecture of UASN

It is well known that energy consumption is more importantin UWSN, which may limit long life cycles. Therefore, thenetwork topology is the basic aspect that needs to be carefully

designed to reduce the serious impact on network perfor-mance. Also, the reliability and capacity of the networkdepend on the network topology. Therefore, how to orga-nize such a network topology is a challenging task, andresearchers need to pay more attention to network topology.Here, the architecture of UASN is classified according to twometrics: one is the motion capability of the sensor nodes, i.e.,stationary, mobile, or hybrid; the other is the spatial coverageof UASN, i.e., 2D or 3D UASNs as shown in Figure 3. Thenodes float freely under the water in the portable UASNswith unpropelled and untethered sensor nodes and driftwith the water current. In UASN with powered sensornodes, the node motion can be controlled by inertial nav-igation systems. Autonomous Underwater Vehicles (AUVs)and Unmanned Underwater Vehicles (UUV) are floats,drifters, gliders, and profiling float, along with examples ofunpropelled portable machinery. Most of these instrumentsare used in oceanography to gather data and measurementsfrom the various layers of the ocean environment. Driftersoperate mostly on the ground and drift with winds and sur-face waves as floats move with the current of the water. Theyare used to acquire measurements from the surface of theocean and send the information to the on-shore center viasatellite or GPS. Gliders are devices driven by buoyancy, asthey can travel vertically comparable to floats for profiling.Besides that, with the assistance of their body and wingdesign structure, they can move horizontally. Sensor nodesare linked to surface buoys or ocean floor units having a fixedposition in the stationary UASNs. For example, the portentrance, stationary UASNs are applied for controlling a cer-tain region. Mobile and stationary nodes coexist in hybridUASN architectures. In [26, 27], using a hybrid architecture,

Underwater localizationtechniques

Centralizedlocalization

Distributedlocalization

Estimated-baselocalization

Prediction-baselocalization

Estimated-baselocalization

Prediction-baselocalization

HL MASL 3D-MASL

ALS CL WPSAAL SL PL MSL

DNRL LDB UPS USP

AFL SLMP

Underwater localizationalgorithms

Range-base algorithm Range-free algorithm

TDoA ToA AoA RSSI Hopcount

Area-base Centroid

Figure 3: Architecture of underwater localization techniques and underwater localization algorithms.

5Journal of Sensors

a portable sink node transverses the network and collectsdata from underwater sensor nodes.

The immediate domain with acoustic energy density iscomparable to the density of the acoustic energy source inthe resounding field and an open atmosphere with an acous-tic energy density using acoustic waves such asWr reflectingthe various wall. Direct field acoustic energy density dependson the distance r to the source associated with:

Wd rð Þ = Ps

4πcr2� �

, ð1Þ

where Ps is the source force and c is the sound velocity. Itimplicitly assumes an omnidirectional source; if the sourceis directional, the direct field relies on the direction.

And the acoustic energy density Wr and the acousticintensity Ir are linked to an isotropic homogenous reverber-ated domain [28] by:

Wr =4Irc

� �: ð2Þ

The derivative of the complete acoustic power in thetank, Q =WrðVÞ, is the variance between the acousticpower that is forced by the Ps source and the acousticpower is degenerated by the absorption or transmissionon the wall aIr .

dQdt

= Ps − aIr = Ps −ac4V Q: ð3Þ

In Equation (3), a is the Sabine coefficient which rep-resents the absorption and transmission due to the walls.When the balance is reached ðdQ/dtÞ = 0, then

Ps =ac4V Q = ac

4 Wr: ð4Þ

It represents the belonging between the source powerPs and the density of acoustic energy in the reverberationsector Wr .

In 2D UASNs, all sensor nodes are supposed to be on thesame depth, e.g., they can be deployed on the seafloor andocean surface, and each sensor node can float at an arbitrarydepth in 3D UASNs [29]. Stationary sensor networks aregenerally regarded to be 2D as the sensor nodes are posi-tioned on the ground buoys or anchors of the ocean floor.The wealth of UASN architecture is due in part to a conven-tional description of UASNs and in part to its application andparticular design criteria. For example, GPS can be used for a2D stationary UASN with nodes deployed on the sea surface,or for similar UASNs with ocean floor units, the sensor nodescan be deployed in predefined location is trivial. Further-more, stationary UASNs do not involve regular localizationas do portable UASNs, which implies that localization proto-cols with comparatively elevated overhead communicationcan still be used as they only operate at the moment of con-figuration. In [30], the authors also proposed the architectureof UASNs and divided it into different groups such as (a)

static architecture, (b) hybrid architectures, and (c) mobileUASNs and free-floating networks.

4. Related Works

In this section, we give a brief description of underwateracoustic communication. After that, we review some widelyknown schemes which are used for underwater localization.Currently, the underwater communication system utilizesEM, optics, and acoustic data transmission schemes to trans-fer data among the various locations of the nodes. EM com-munication scheme is influenced by the conducting nature ofthe underwater environment while optic waves are only ableto move on very short distance because optic waves are easierto absorb in underwater environments [31, 32]. Therefore, anacoustic communication scheme is only one scheme that hasbetter performance as compared to EM and optical due toless attenuation in the underwater environment. Acousticsignals also have less attenuation in the deep and thermallystable underwater field. Acoustic signals attenuate more inshallow as compared to deep water because of the tempera-ture, noise, and multipath reflection and refraction. In theunderwater field, the sound speed is not constant instead ofthis sound speed varies almost at every point. Near to thewater surface, sound speed is almost 1500 that is four timeshigher than the sound speed in air and very slow as comparedto the EM and optic speed in air.

Due to the unique challenges of the acoustic channel, itis highly variant, for example, high propagation delay, var-iable sound speed, narrow bandwidth, reflection, andrefraction. Because of these unique properties, it createsmore issues regarding MAC protocols. MAC protocols havetwo main groups such as content-based and scheduled-based protocols. Content-based nodes complete each otherfor the exchange of signals, while scheduled-based avoid col-lision among the transmission nodes. Content-based are notsuitable for the underwater environment, while scheduled-based such as TDMA and FDMA are not efficient due tothe high propagation delay and narrow bandwidth, respec-tively; however, CDMA is appropriate for UANs [33, 34].

A localization scheme for UWSN is presented for locali-zation issues in large-scale UWSNs. Unlike in TWSN, GPScannot work properly in underwater or attenuate highly[35]. Due to the costly equipment of underwater, limitedbandwidth and harshly impaired channel all make the pro-cess of localization very challenging. Currently, most of thelocalization techniques are not well appropriate for the deepunderwater field. The researchers presented a new schemethat mainly consists of four types of sensor nodes, such asDETs, surface buoys, ordinary nodes, and anchor nodes.DET is connected to the surface buoys and can dive and riseto the water surface for the broadcasting of its location. Sur-face buoys are supposed to connect with the GPS. Anchornode can estimate their locations based on location informa-tion from the DETs and estimation of the distance to theDETs. This localization scheme is scalable and can be appliedto make balances on the accuracy and cost of localization.

In [36], the authors have presented a new SLMP localiza-tion technique with the prediction of mobility, for the large-

6 Journal of Sensors

scale USNs. In this scheme, by taking benefits of the inherenttemporal correlation of the mobility of objects in underwa-ter, anchor sensor nodes conduct linear prediction. Everyordinary node guesses their position by using the spatialcorrelation of object mobility pattern in underwater andweighted-averaging its received motilities from the othersensor nodes. Simulation results of the new scheme showthat SLMP can highly minimize the cost of communicationwhile keeping constant relatively high accuracy and cover-age in localization. The authors also estimated the impactof different design parameters, such as prediction step, con-fidence threshold, prediction window, and prediction errorthreshold, on the performance of localization.

A comparison of various localization techniques is stud-ied above and some in the next coming section which is alsoshown in Table 2. Localization techniques are comparedbased on sensor node mobility, range estimation, time syn-chronization, localization accuracy, network lifetime, linkquality, etc. The localized nodes must be time-synchronizedif ToA is applied for the range estimation. Localization tech-niques such as silent positioning are useful in reducing thecommunication overheads because nodes only receive infor-mation and do not transmit any information for localization.As compared to the receiving side, transmission utilizes moreenergy. Recursive localization is more beneficial to increasecoverage. Only for routing protocol, if the localization of

Table 2: Performance evaluation of underwater acoustic networks and underwater localization schemes.

Ref.Energy

consumptionNetworklifetime

No. ofnodes

Timesynchronization

Packetexchange rate

Loca.accuracy

Comm.overhead

DelayError

estimationLinkquality

[3] X ✓ ✓ — ✓ ✓ X ✓ ✓ ✓

[18] — — ✓ X ✓ ✓ — — ✓ —

[21] ✓ X X X X ✓ — X ✓ X

[26] X — ✓ ✓ ✓ X ✓ ✓ X —

[41] X — ✓ — ✓ ✓ — ✓ X —

[42] — X X ✓ X ✓ X — ✓ —

[43] X — ✓ ✓ ✓ X — X ✓ —

[44] — — X ✓ ✓ ✓ ✓ — ✓ X

[46] X ✓ ✓ — ✓ ✓ — ✓ ✓ —

[47] X ✓ ✓ — ✓ ✓ X X X ✓

[37] X ✓ ✓ ✓ ✓ ✓ X X X ✓

[48] X — ✓ — ✓ ✓ X X X ✓

[49] X — ✓ ✓ ✓ — — ✓ — X

[51] X X ✓ X — X X — ✓ —

[53] X ✓ ✓ X ✓ ✓ X X ✓ ✓

[56] X — ✓ — ✓ ✓ — ✓ ✓ —

[57] ✓ — ✓ X ✓ X X ✓ ✓ —

[58] X — — — X X X — ✓ —

[59] X — ✓ X ✓ ✓ X X ✓ X

[61] X — ✓ X ✓ X X ✓ ✓ X

[62] X ✓ ✓ ✓ — ✓ ✓ — — —

[63] X — ✓ X ✓ ✓ X X — X

[65] — — ✓ X X X ✓ ✓ ✓ —

[67] X ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ X

[68] — — ✓ — ✓ X — — ✓ ✓

[36] X X ✓ X ✓ ✓ X X ✓ —

[73] X ✓ ✓ X ✓ ✓ — X ✓ X

[74] — — ✓ X X ✓ X X ✓ —

[75] X ✓ ✓ X ✓ X ✓ ✓ ✓ ✓

[77] X ✓ ✓ X ✓ ✓ X X ✓ ✓

[79] X — — ✓ ✓ ✓ X X ✓ —

[85] X — ✓ ✓ ✓ ✓ — — ✓ —

[89] ✓ — ✓ X X ✓ ✓ ✓ ✓ X

[91] X X X X X ✓ X — X X

[94] — — ✓ X ✓ ✓ — — ✓ —

[97] ✓ — ✓ — ✓ ✓ X — ✓ —

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sensor nodes is required, then any of the range-free tech-niques can be applied. Any localization technique can beapplied according to the requirement of the application.Also, a performance-based comparison of LSL, PL, andDNR localization techniques is presented in [37]. However,these schemes encounter localization shortcomings. Theaforementioned schemes provide a brief description of local-ization and still many important aspects need to be consid-ered in underwater localization. Therefore, this review canprovide a detail explanation of all localization techniques,the existing challenges which face underwater localization,and the future direction for the solution of these challenges.

5. Techniques Used for UASN Localizations

Briefly, localization of GPS-based algorithms has been pro-posed for terrestrial WSNs that, for reasons such as high-frequency GPS, cannot be applied directly to UASNs thatattenuate underwater quickly and cannot reach the sensornodes several meters below the water surface. Also, GPS-less systems of localization implement high overhead com-munication [38, 39]. For the above reasons, it is not possi-ble to directly apply WSN localization to UASNs. In [40],the author’s presented the adaptations of the “stochasticproximity embedding algorithm” and “multidimensionalscaling algorithm” for UASN. These algorithms conductconcurrent localization of all target nodes compared to thestraightforward localization algorithm based on ray tracingthat conducts sequential localization of each target node.The proposed methods of localization take into account thebending of acoustic rays and thus, when deployed in realUWSN, give good accuracy. The algorithms have beenadjusted to offer directly absolute locations. There are manylocalization techniques; some of them are categorized as cen-tralized and distributed methods of localization. In central-ized localization techniques, firstly, estimate the position of

every sensor node in a control center or sink and the sen-sor nodes do not know their location unless the sink orreference node explicitly sends this information to thenode. In this technique, the sensor nodes may be localizedat the end of the process such as in the postprocessing phase,or they may gather data periodically for the tracking of sensornodes. The distributed localization technique allows eachsensor node to do localization individually; they are free todo individually localization independently. Centralized anddistributed localization techniques are divided into subcate-gories, i.e., estimation- and prediction-based localization. Inthe estimation-based localization, the latest available infor-mation is used to obtain the current location of the sensornode. For the prediction-based localization, the previousnode location, distance information, and anchor locationare used to predict the sensor node located at the nextmoment. Therefore, it is appropriate for mobile UWSN ora hybrid UWSN.

5.1. Centralized Localization (CL). In this technique, first, itcalculates the position of each sensor node in the control cen-ter or sinks, and the sensor node initially does not knowits location unless the sink sends this information explic-itly. In this method, nodes may be located at the end ofany operation, i.e., postprocessing phase or data may be col-lected sporadically for sensor node monitoring. In central-ized algorithms in [41], in which a key organization (e.g.,the control center) exists that gathers all the needed data orestimation (e.g., estimated distances between the nodes incommunication ranges and calculates distances to the anchorsensor nodes) and centralizes the sites for the sensor nodes.After the central organization determines the location ofthe sensor nodes, it sends the location information back tothe corresponding sensor nodes. Sensor nodes interact in acentralized algorithm through a base station that includesone cell as shown in Figure 4.

S6

S7

S8

S9

SnS1

S3

S4S2

S5

Basestation A

Basestation B

Cell A Cell B

Figure 4: Centralized network topology.

8 Journal of Sensors

5.2. Estimation-Based Localization. Estimation-based local-ization techniques are further divided into subparts bythe following:

(1) Hyperbola-Based Localization (HL). Using the locali-zation method based on hyperbola, the location of asound source, i.e., a target such as mammals, can beidentified in the oceanographic scheme using hydro-phones such as sensor nodes with known locations[42]. HL also adapts standard oceanographic soundsource localization issues to standing 2D UASNlocalization. In HL, the sensor node sends wide-range signals to the anchor node about 1 km and acentralized sensor node estimates its location. Refer-ring to [43], the authors present a new approach toa better localization accuracy for UASNs. This systemuses the hyperbola-based strategy to detect eventplace and a normal distribution to estimate and stan-dardize error modeling. This efficiency of the methoddemonstrates a separate enhancement over the esti-mation of the place of the frequently used minimumsquares based on the circle. In terrestrial applications,a multi-iteration and least square measurement sys-tem are often implemented to find a decent estimate[44]. But this system is not useful in underwaterapplications due to its heavy communication costs.At the same moment, it was noted that distance mea-surement errors often follow a certain shape that canbe implemented to further improve the precision ofthe localization. Authors evaluate and use distribu-tions of measurement errors to improve the precisionof localization

(2) Motion Aware Self-Localization (MASL). Due to thelong delay in the propagation of signals in theunderwater environment, it may take a longer timeto collect the number of distance estimates whichis required for localization, thus increasing the pos-sibility of obsolete information. MASL’s primaryobjective is to discover the faults in the estimatesof distance and provide a precise scheme of locali-zation. The underwater sensor node collects rangeestimates between the sensor nodes of the neigh-bors and itself in the MASL method. The distanceestimation is performed through certain iterationsperformance. At each iteration, the algorithm pol-ishes location distribution by distributing the fieldof an event into smaller grids operation and select-ing the area in which nodes reside. In [45], theauthor models the ocean current as layers of equaldensity, variable velocity, and the nodes of the sen-sor move with those underwater currents

(3) 3D Multipower Area Localization Scheme (3D-MALS). 3D-MALS varies from the MASL method,which combines the thinking of anchor nodes[46] with a variable rate of transmission energy andthe thinking of anchor nodes with vertical mobilityof buoys house mechanical device [47] that operatesas an elevator for underwater transceivers called

Detachable Elevator Transceiver (DET). DET broad-casts its set of GPS-driven coordinates at differentconcentrations of energy and then goes down under-water. The unlocalized sensor nodes can retrieve theposition of portable anchor nodes and their, respec-tively, smallest energy concentrations and then sendthem to the reference or sink node. The referencenode can understand the position of each sensornode after gathering data

(4) Area-Based Localization Scheme (ALS). It is a type ofscheme that provides an estimate of where the sensornode is in the area of the sensor instead of the preciseset of coordinates. Anchor nodes in ALS divide theoperating region by sending messages to nonoverlap-ping areas at different energy levels. ALS is appropri-ate for the setting where there is no need for accuratelocation data and when the anchor nodes can changetheir level of transmission power. The benefit of ALSis that the received signal strength (RSS) is jointlylight, range-free, and no synchronization require-ment. For applications demanding internet locationestimation, ALS is not appropriate, so it is not appro-priate for precise localization. In [37], ALS is an algo-rithm for USNs that is range-free, centralized, andcoarse-grained. A sensor node underwater maintainsa list of anchor nodes and associated energy concen-trations. This data is sent to the reference or sink bythe sensor node and the sink discovers the region inwhich the sensor nodes reside. ALS offers a coarse-grained localization evaluation and it is a centralizedlocalization. Therefore, it is not suitable for large-scale USNs and applications that require accuratelocation estimation. After offering all the regions(one for each anchor node) in which it resides, a pri-mary server offers the sensor node position assessment[48]. On the other hand, USP is a 3D localizationalgorithm with internal position graininess comparedto ALS (i.e., it measures the position of a node withina coordinate system as protecting a location within asubarea). In [49], the authors suggest that only asupper sensor node transmits information from itsneighboring sensor nodes and is then shared withnearby sensor nodes. Using this protocol, the packetcollision in the network is obviated during node dis-covery. Also, only seed nodes can make additionalresults; the remaining nodes in the network do notinclude power consumption to transmit text messagesto their other neighbor nodes. Farthest/Farthest algo-rithm, Farthest/Nearest algorithm, Nearest/Farthestalgorithm, and Nearest/Nearest algorithm are thealgorithms used to select additional seed nodes [50]

5.3. Prediction-Based Localizations

(1) Collaborative Localization (CL). A CollaborativeLocalization (CL) scheme [51] consideration of por-table UASN applications is where underwater sensornodes are accountable for gathering ocean depth

9Journal of Sensors

information and are accountable for transferring it tothe water surface. This architecture uses profilers andsupporters of two kinds of underwater sensor nodes.These two kinds of sensor nodes come down under-water, but profilers come down ahead of them, i.e.,deeper than other nodes. The distance between pro-filers and followers is periodically evaluated using theToA to place the profilers to followers. In particular,the CL algorithm weighed time synchronization algo-rithm with elevated transmitting delay and a multi-path acoustic propagation channel for UASN. Byusing the new time synchronization protocol, the tar-get position is evaluated using maximum likelihood(MLL) techniques based on the distance of arrival(DoA), since the statistical model between the realand measuring location is based on the marine chan-nel signal envelope. The suggested algorithm also fol-lows the distributed-centralized computing operationthat minimizes the energy transmitting node in USNs

(2) Distributed Localization. The distribution localiza-tion method allows each sensor node to locateseparately or nodes are free to locate such as neigh-borhood distance, anchor position, and connectivitydata and then send all these data separately to the ref-erence or super node. On the other hand, instead ofbeing placed in one central entity, the function oflocation finding is distributed to the sensors them-selves in the distributed localization algorithm. In adistributed network, sensor nodes communicatethrough peer-to-peer (P2P) as shown in Figure 5.

Distributed positioning algorithms usually assume thatanchor sensor nodes are randomly distributed throughout

the sensor network, and the percentage of anchor nodes inthe network is also high (5%-20%). Deploying anchor nodesin terrestrial sensor networks is not a challenge because aGPS-equipped node can act as an anchor node [52]. How-ever, in the case of an underwater field, the network estab-lishes the backbone of the randomly distributed anchorsuch as ABC nodes; the exact location of which is knownbefore is not a trivial issue

5.4. Estimation-Based Localizations

(1) AUV-Aided Localization (AAL). An AAL-basedapproach is presented in [53] for a hybrid 3D UASNwith stationary underwater sensor nodes and AUVtraveling in the UASN sector. Using the dead-reckoning method, the AUV can get its positionunderwater. Dead reckoning with the costly inertialpiloting machinery is feasible and the position hasbeen periodically calibrated. The AUV goes to thewater surface for this purpose at certain periods toachieve GPS coordination from a satellite. A wake-up message can be broadcasted from a separatepoint on its moving path during the AUV operationcycle. It occurs when AUV receives this signal fromthe underwater sensor node, it begins the localiza-tion action by transmitting a request signal to theAUV, and AUV responds with a reply signal. Thepair of requests and response packets provide atwo-way algorithm and the response packet includesthe AUV coordinates so that the underwater nodeuses the lateration process to measure its self-location after the exchange message from three dif-ferent noncoplanar AUV positions (see Figure 6).

Cluster 3Cluster 2Cluster 1

S1

S2

S3

S4

S5

S6

S7

S8

Sn

Base station

Figure 5: Distributed network topology.

10 Journal of Sensors

AAL utilizes a two-way display to alleviate the needfor synchronization, but on the other side, a sensornode can invest in a silent algorithm more energythan it does and also boosts the protocol’s overheadcommunication. AAL’s precision is also influencedby the AUV’s localization calibration frequency. Anassessment of an AUV’s single beacon localizationproblem modeled as a double integrator [54], whereits input is the acceleration in an inertial referenceframe and its output is its range to a static beacon.The nonlinear map between range and positionenables the range-based observability problem to beconsidered nonlinear. Two complementary problemsare discussed here in the observability evaluation:firstly, the local weak observability of the nonlinearsystem and secondly, the worldwide observability ofa linear time-varying system representation obtainedby a worldwide technique of increase

AUVs emit an omnidirectional beacon [55]. WhenAUV crosses the sensor node at t1, this nodereceives a signal that can be used for the distancecalculation d1 between the AUV and the sensornode. Similarly, the distance d2 can be calculatedusing the ToA technique as shown in Figure 7. Toget the sensor node’s coordinate, it requires theAUV’s coordinates at two distinct time instants.The location of the node is chosen based on the easytriangulation operation. Also, AUV routing is verycomplex to guarantee that two necessary beaconmessages can be acquired from AUV for each node.Differences in time indicate to some extent the dis-tance. The method is based primarily on the time

difference [56]. At distinct locations, AUV broad-casts its coordinates. When receiving messages frommore than three noncollinear AUV locations, theunderwater nodes estimate their place laterally. Thistechnique has a large delay in localization due to theslow velocity of AUV, which is why it is more usefulfor stationary USNs than dynamic USNs.

(2) Silent Localization (SL). There are three kinds ofmessages in AUV-assisted localization systems:wake-up, demand, and reaction messages. The pro-cess of positioning consists of three steps [57]. TheAUV sends a wake-up signal when it joins thesensing operating region. All sensor nodes willsend a request signal or packet after getting thewake-up signal

The AUV then responds with a response packet thatcontains the coordinates of AUV. For this stage, each

Two-way messageexchange

AUV (t1)AUV (t3)

AUV (t0)AUV (t2)

Figure 6: AUV cycle of operation with two-way message exchange.

d1d2

AUV

t2(x2, y2)

t1(x1, y1)

S

Figure 7: Omnidirectional AUV-aided localization.

11Journal of Sensors

detector node shall communicate with the AUV atleast once. Therefore, it is familiar to consume extraenergy for localization. Due to the energy consump-tion of communication between the sensor nodesand the AUV, a better alternative is that during thelocalization era, only beacons are received by the sen-sor nodes without interacting with others. This sortof strategy is called a technique of “silent localiza-tion.” Beacons are interchanged between the sensornodes and the AUV in the past methods. Lastly,silent localization can considerably decrease under-water localization power consumption.

(3) Dive and Rise Localization (DNRL). Refer to [58], tobetter define DNRL. It is a distributed, estimation-based localization protocol that applies mobileanchor using the localization of underwater sensornodes, and these anchor nodes are named as bea-cons called “Dive‘N’Rise (DNR).” By using the samehydraulic laws as the profiling floats laws and regu-lations, DNR can descend and ascend. When DNRreaches the water’s surface, it utilizes GPS receiversand reaches its GPS coordinates while floating onthe water’s surface. After that dive again, theybroadcast their coordinates at several periods untila precalibrated underwater depth. Mobile anchorsclimb up to the water surface in the first round oflocalization to obtain the updated coordinates fromGPS. They descend and dive or ascend periodicallyon the second-round journey and so on until theend of the UASN phase. Underwater sensor nodeslisten to time-stamped DNR texts and use a ToAmethod to evaluate distances to DNR beacons usingone-way ranges. The range estimates and the coor-dinates of the anchor node are used in lateration.One advantage of DNRL is the silence that resultsin low overhead communication and very highenergy efficiency. DNR has a wide coverage andgives an accurate estimate as the mobile anchorsdive into the vicinity of the underwater nodes andperiodically update their position when they reachthe surface of the water. On the other hand, for highlocalization effectiveness, DNRL needed a big quan-tity of DNR beacons, while DNR beacons areexpected to be more expensive than other underwa-ter sensor nodes due to their movement ability

(4) Proxy Localization (PL). PL utilizes the DNRLmethod to locate the top of the network location.The DNR beacons sink to half of the 3D USN depth.Localized nodes then become proxies of location for

those nodes that float at greater concentrations.Location proxies advertise self-coordinates for fur-ther localization in proxy place. Nonlocalizedunderwater nodes can later use and locate them-selves with the proxy coordinates. A nonlocalizedunderwater sensor node picks the trusted proxiesbetween nodes using the hop count metric. Hopcount is the distance from a proxy node to a beacon.Error accumulates in iterative methods of the local-ization at the proxy nodes remote from the beacons.Therefore, proxy nodes with the lowest hop rangecan be selected to enhance the accuracy of laterationequations as shown in Figure 8

(5) Localization Using Directional Beacons (LDB). LDBis suggested for a 3D UASN hybrid in which station-ary underwater sensor nodes are located by AUVlike to AAL [59]. When AUV reaches the water’ssurface, it gets its coordinates from the GPS, divesagain to a certain depth, and performs dead-reckoning for underwater self-localization. LDB dif-fers from AAL during the localization operation in away that the AUV travels above the operation areaas shown in Figures 9 and 10. It utilizes a directionalacoustic transceiver to transmit its position and thetransceiver angle. The sensor node uses angle datato map the AUV coordinates with itself to the samehorizontal plane. After two or more beacons nodesare obtained, the nodes can assess the location infor-mation. The sensor nodes listening to the beaconnodes and the beam form distinct circles h. The cen-ter of the circle is (x, y, h). Thus, the circle radius canbe expressed as follows:

r = tan α

2 × Δh, ð5Þ

where α is the angle of conic beacon and Δh =∣ha − h∣. The rough position by using the receivingbeacons (x, y, h) can be estimated [60].

On the other side, LDB is a range-free, silent locali-zation method that is more energy-effective thanAAL’s method. LDB has one disadvantage that theAUV is restricted to traveling above the UASN area,which may be impossible in a real situation. Fur-thermore, owing to hitting with each other, the fre-quency of the AUVmessages affects the precision ofthe localization process

(6) Multistage Localization (MSL). To the best of ourunderstanding, the authors suggest the MSL systemthat defines the DNRL-related by adding coverageand delay an additional localization stage and usingeffectively located underwater nodes as anchornodes. An unlocalized node utilizes the coordinatesand range estimation from three noncoplanar sensornodes that can be DNR beacons or a localized sensornode of the underwater sensor. Due to the iterativelocalization method, MSL has the disadvantage of

0 bits

X-coordinate Y-coordinate Z-coordinate

Time-stamp MOD HC

32 bits 64 bits 96 bits

Figure 8: Localization packet format for proxy localization.

12 Journal of Sensors

its high overhead communication. For this purpose,therefore, MSL is less energy-efficient than DNRL.Furthermore, localized underwater nodes in MSLprovide their estimated locations that already includefailures in the estimate. Error accumulates at nodesusing coordinates of localized underwater nodesinstead of coordinates of anchor nodes. Because ofits one-way ToA algorithm, comparable to the DNRLalgorithm, MSL also required time synchronization

(7) Underwater Positioning System (UPS). UPS is a sys-tem used for monitoring and piloting underwatervehicles or divers through measurements of acousticrange and/or direction, and subsequent triangula-

tion of position. UPS is frequently used underwater,including oil and gas exploration, sea science, rescueactivities, marine, law enforcement, and militarypurposes. The authors suggest UPS, an extensionof the terrestrial WSN localization system intro-duced in [61]. UPS is a localization system basedon TDoA for standing UASNs. It utilizes fouranchors that transfer beacon messages sequentially.One anchor node works as a master anchor and ini-tializes the procedure of localization. Assume themaster anchor is selected as the “A” anchor (seeFigure 11). This signal is heard by anchor “B” andsensor node “S” when the beacon signal is sent.Anchor “B” reacts to anchor “A” by recording thetime difference between anchor “A” beacon arrivaland the beacon signal transmission time. Theanchors “C” and “D” repeat the same successivecycle after the “B” anchor. Node “S” hears these bea-cons of anchor and calculates the TDoA of beacons.Then, by multiplying them with sound velocity, ittransforms TDoA values to range distinctions. NodeS is presumed to understand the locations of theanchors and measure self-location using anchorposition and trilateration equations range distinc-tions. Since UPS utilizes TDoA, synchronization isnot necessary. The writers also suggest the UPS,i.e., a silent acoustic positioning system for theunderwater vehicles/sensors, which depends on the

AUV

Underwater

sensor nodes

Satellite

Water surface

AUV with

directional

transceiver

Figure 9: AUV with directional beam in the LDB scheme.

Sensor node

AUV trajectory

(xAUV t2, yAUV)(xAUV t1, yAUV)

Figure 10: Sensor’s localization in LDB.

13Journal of Sensors

ToA of RF signals for location estimation from threeanchor nodes. UPS comprises essentially two steps:in the first step, from four anchor nodes, they candetect variations in signal arrival times. These timedistinctions are converted from the underwatervehicle/sensor to the anchor nodes into range dis-tinctions. In the second step, the transformation ofthese distance estimates into their coordinates takesplace through trilateration. The authors suggestedthat UPS silent placement should be further empha-sized. First, because sensors/vehicles do not transmitany beacons for positioning purposes, it can consid-erably retain bandwidth and therefore modify net-work throughput. Second, UPS also applies four ormore anchor nodes to an asymmetric UASNs wherethe underwater vehicles or sensor transmission couldnot reach. Third, silent positioning offers powerfulprivacy of location that can assist safeguard sensors/-vehicles from detection in critical circumstances

(8) Wide Coverage Positioning (WPS). The UPS algo-rithm may not uniquely locate all sensor nodes inthe four anchor nodes operating region [62]. It indi-cates that the sensor nodes near the anchor nodesneed five anchors to fix the issue. In the event ofWPS, four anchors are used whenever distinctivelocation can be achieved utilizing four anchorscalled UPS (4); otherwise, WPS will use five anchors(UPS (5)). UPS (4) and UPS (5) are used together tosolve the overhead and price of communication forthe sensor nodes with four anchors that can alreadybe located. These nodes consume the same numberof energy as the initial system of localization

(9) Underwater Sensor Positioning (USP). Using USP,for underwater localization, underwater nodes arefictitious to equip with the help of pressure sensornodes by which nodes find their depth position.The depth data is used by an underwater node tomap the accessible anchors on a horizontal plane

on which it rests. While mapping from 3D to 2D,some of the anchor nodes maybe reside in overlap-ping locations. In these situations, an underwaternode picks out another set of the anchor’s sensornode. Localized underwater nodes transmit theirlocation data at each iteration of USP and burntheir position estimates based on getting messagesfrom the nodes of neighbors. The unlocalized nodesattempt to constitute localization using only twoanchors nodes; the process is called as bilateration.If the two anchors do not compute a new location,the node will wait until it hears from other neigh-bors’ nodes they are already localized. After a slum-ber period that is preconfigured timing, the samelocalization operation is reinitiated. In [63], authorspresent a positioning system in underwater sensornetworks. For this, the writers implemented aweighted Gerchberg-Saxton algorithm to addressthe multipath acoustic propagation problem of var-ious feasible distance measurements between twonodes. A standard positioning technique finds therange in between two sensor nodes which are basedon either on initial arrival or the stronger way, butneither may agree to the immediate acoustic routein underwater. In WGSA, based on the ML rules,it can be used for identification of direct path fromthe overall existing multipaths by connecting eachpath with a weighting component; it can be under-stood in a certain way roughly as the chances of thatpath existence the immediate path

In the WGSA algorithm, the weighting componentand the sensor node location are updated periodi-cally or based on iteration, with each iteration com-putationally easy. USP [64] is based on sensornetwork technology, which uses many conventionalhydrophone stations and GPS sensor nodes abovethe water surface. Referring to [65], a new underwa-ter acoustic positioning system uses new terminolo-gies. For the range calculation unit, the distance canbe calculated alternately based on the time differ-ence by using the connection between propagationnoise loss and propagation distance. It is a verysimple procedure for processing, and it can use tomeasure a long-distance winder medium. For anunderwater robot, a system of acoustic positioningunderwater is shown in Figure 12.

In [66], the geometric configuration of a surfacesensor network will maximize the variety of infor-mation associated with the target positioning algo-rithm underwater in a well-defined sense. Becauseof the white Gaussian noise, the range estimate isnot precise and its discrepancy depends on distance.

The Fisher Information Matrix (FIM) and its deter-minant maximization are used to evaluate the con-stellation of sensor nodes providing the target’smost precise location. Another approach is to locat-ing and mapping Underwater Robotic Fish (URF),

A

Sensor node

B

C

D

Figure 11: Underwater positioning system (UPS) using fouranchors.

14 Journal of Sensors

which is based on both the Particle Filter (PF) sys-tem for cooperative localization and the OccupancyGrid Mapping Algorithm (OGMA). The suggestedCLPF is more advantageous to use the probabilisticalgorithm that they do not have previous data orinformation about the URF model is needed toaccurately attain a 3D localization. If the numberof mobile beacon nodes for some traditional locali-zation algorithms is less than four, which is the min-imum number, it can achieve good accuracy in thecase. To build the environment map, the localiza-tion result of CLPF is fed into OGMA. Moreover,even under a normal degree of connectivity, USPhas reduced localization achievement than the othersurveyed localization methods

(10) Anchor-Free Localization (AFL) Scheme. Conven-tionally, the existing localization algorithm mostlypresumes that the network has a multitude of anchornodes [67], for the assistant of node positioning.Mostly, it makes the use of AUV or sometimes a sen-sor node with a particular device, an anchor node,and AFLA. AFLA is especially intended for underwa-ter networks with active restrictions. In the case ofAFLA, they do not need anchor node informationand use the data of its neighbor’s sensor nodes. In[68], the author’s present another algorithm for thedifficulties in localization that license for node unre-lated discovery on the Line-of-Sight (LoS) and anyrigid reference sensor nodes. They create a surface-based reflection anchor-free localization (SBRAL)method in which all nodes use homomorphicdeconvolution to establish a reflected communica-tion connection from the water surface. A GPS-free protocol [69] can discover nodes and relative

location of nodes. The procedure of node discoverystarts with first or an initial seed node with a knownposition or location. The first seed node can deter-mine the relative positions of neighboring nodeswithout using inner information and is similar tothe first node, other nodes in the network. Self-initialization includes certain distant nodes becom-ing seed nodes for further cycles of discovery andso on. Node is originated by seed node S1, then S1broadcast the message among its neighbors andreceive response from different nodes, and thenselect a second node S2. S2 revise the same proce-dure, broadcast, and receive, then S3, and so on.Finally, the sensor nodes in the intersection regionof these three seed nodes can determine their posi-tion using the trilateration algorithm as shown inFigure 13. Also, owing to the maximum delay inpropagation in the underwater medium, the nodediscovery process may take a long time owing toAFL based on communion range estimates amongits neighboring sensor nodes. And the approach tothe target station is being viewed as a survivorthrough the RSSI calibration and RDD without apremeasurement. This algorithm’s weakness is fixedby using a moving distance for the PLE assessmentinstead of the known distance. By using the remain-ing counter that has a direction of movement until itexceeds, it is an indication to change direction

5.5. Prediction-Based Scheme

5.5.1. Scalable Localization with Mobility Prediction (SLMP).SLMP is a method that uses surface buoys, anchor nodes, andcommon nodes [36]. By using the prior coordinates and theirmobility pattern, the anchor node measures its position. As

GPS,CPUdistance measurement

unit

GPS, CPUdistance measurement

unit

GPS, CPUdistance measurement

unit

A control station(online communication)

Underwaterrobot

Sensor

SensorSensor SensorSensor

Figure 12: Underwater robot-based sensor positioning system.

15Journal of Sensors

the mobility pattern may cease to be used in time, the anchornode periodically checks the pattern’s validity. Updatesanchor trigger when the model is no longer valid. GPS coor-dinates are received by the ground buoys and sent to anchornodes. After predicting its position, an anchor node usescoordinates of surface buoys and lateration measurementsof distance to buoys and estimates their position. If theEuclidean distinction is less than a limit between the expectedand estimated position, the anchor node will consider itsmobility model valid, depending on the mobility pattern,overhead communication, and energy consumption inSLMP. SLMP uses a temporally and spatially connectedmodel of mobility representing the tidal current in shallowwaters. Because of this correlated movement, SLMP neededfewer updates, resulting in low overhead interaction andpower consumption. Anchor nodes periodically predict theirposition and through distance measurements check the accu-racy of their predictions to surface buoys. They update theirmobility pattern if the prediction is incorrect and send a sig-nal to the ordinary sensor nodes. With their mobility model,which is updated when anchor nodes announce an updatealert, ordinary sensors predict their location.

The algorithms are further classified into range-basedand range-free [70].

6. Range-Based Algorithm

Accurate estimation of distance or angle measurement ismade in the range-based algorithm, and TDoA, ToA, andAoA are the algorithms used for this purpose. Due to certainconstraints like its time-varying characteristics, which arerarely used in UASNs, RSSI is not too convenient. TDoAemploys arrival time difference and is the time differencebetween distinct transmission mediums or beacons from dis-tinct reference nodes used to assess the distance between twoobjects. Similarly, ToA is the time of arrival using for distanceestimation. In the suggested range-based algorithms [71], themost frequently used technique for UASNs is the ToA algo-

rithm, and it is favored in UASNs as compared to terres-trial. It is several times smaller than the radio signal inthe atmosphere owing to the sound velocity in water.ToA is mostly implemented to UASNs, although synchro-nization among nodes was needed by ToA. A hybrid bear-ing and range-based UWSN has been studied in [72]. Theauthors explore the impact of comparative sensor-targetgeometry on the underwater target location’s prospectiveresults. The optimality criterion function is built as theFisher Information Matrix (FIM) determinant, and themean square error (MSE) is also provided with a compa-rable assessment. The MSE is minimized only if, underthe assumption of a set distance between the detectors tothe underwater goal, the determinant of the FIM is maxi-mized. The authors suggest a localization method called aTwo-Phase Time Synchronization-Free Localization Algo-rithm (TP-TSFLA) in [73]. TP-TSFLA comprises of twoalgorithms, the algorithm of range-based assessment andthe algorithm of range-free. The writers discuss a timesynchronization-free localization system in the first algo-rithm that is based on the method of Particle Swarm Optimi-zation (PSO) to achieve unknown node coordinates. In thesecond algorithm, authors present a Circle-Based Range-Free Localization Algorithm (CRFLA) to locate the unlocatednodes that cannot acquire position information through thefirst phase. For the second phase, finding the location is theconduct of those sensor nodes situated in the first phase suchas helping the new anchor nodes. Below is a comprehensiveTDoA, ToA, AoA, and RSSI description.

6.1. Time Difference of Arrival (TDoA). Localization is one ofthe main issues in a network of wireless sensors. The TDoA isa commonly used method for localizing underwater. By usingthe TDoA method, a goal with anchor nodes can be asyn-chronous [74]. An asynchronous ToA-based localizationmethod is used [75–77], in which the transmission sourcetime is unknown and ToA measurements have a favorableprejudice due to the synchronization mistake which can leadto a big localization mistake. One method is to use TDoA-based measurement to fix this issue, which does not rely ona transmitter source’s transmission time. They also usethe method of SDP to turn the nonconvex MLE issue intoa convex issue. Since GPS signals are extremely attenuatingunderwater, it is necessary to develop a precise range-basedalgorithm for locating underwater. Authors define a sequen-tial method for time synchronization and localization in theacoustic channel [78]. Authors consider it a realistic situationin which nodes are not time-synchronized and underwatersound speed is also unknown, validating the issue of localiza-tion as a series of two linear estimation issues. The velocity ofpropagation that changes with depth, temperature, salinity,etc., anchor nodes and unlocalized (UL) nodes cannot beregarded as time-synchronized, and the nodes of the watercurrent are constantly moving or their self-motion. Theauthors define a fresh sequential algorithm in an underwatersetting for joint time synchronization and location. Thisassociated method is based on exchanging informationbetween the anchor and UN nodes, using the directional nav-igation algorithm used in nodes to achieve precise short-term

S1

S2

S3

Sensor nodesoverlapping area

Figure 13: AFL using three seed nodes.

16 Journal of Sensors

estimates of movement and using continuous nodes. Thismethod achieves a precise localization environment is utiliz-ing only two anchor sensor nodes and exceeds the bench-mark systems when node synchronization and propagationspeed data are unknown.

6.2. Time of Arrival (ToA). In ToA-based systems, the targetmust be symmetric with anchor sensor nodes. The author’sproposed a new model [79] called ToA-Based Tracked Syn-chronization (ToA-TS) expands GPS as localization to out-line where beacon signals do not coincide and monitor thesubmersible while synchronizing time. And the beacon thattransmits a signal will include the beacon’s location datagathered from GPS, as well as the time stamp that representsthe time it was sent based on worldwide time. On the receiverhand, the message’s receiving time is saved according to thesubmersible’s local clock. In [80], the investigator introducesa mixed localization algorithm for Time-of-Flight (ToF) andDirection-of-Arrival (DoA) which is desirable in the settingof shallow underwater control applications as well as harbormonitoring operations. Localization of both ToF and DoAcan be measured using a single-way range operation. Then,we need to combine ToF and DoA to reduce the number ofthe reference node. ToF measurements are performed by cal-culating the transmission propagation time between themoored reference antenna at the bottom of the tank and anantenna array. The DoA measurement is performed by eval-uating the signal touching DoA angle on the antenna array.For real-time positioning in [81], a cooperative low overheadmonitoring method for portable underwater networks mea-sures vehicle location in real-time circumstances. Wherenodes follow more predictable paths, the efficiency of local-ization can be improved by strategically positioning beaconsmost importantly, so in this scenario, all cars are likely tohear a beacon periodically. A joint localization and timesynchronization solution considers the underwater environ-ment’s stratification impact; thus, compensating for theprejudice in the distance calculation induced assumingsound waves travel underwater in straight lines. The preci-sion of both is greatly enhanced to combine time synchroni-zation and localization [82]. The issue of locating anunderwater sensor node in [83, 84] is investigated by authorsbased on message broadcasting from various ground nodes.With the ToA readings from a multicarrier modem basedon SDP, each sensor node can locate itself to the receivernodes on the grounds of travel time differences between var-ious sensor nodes.

6.3. Angle of Arrival (AoA). AoA is a distributed localizationand orientation algorithm, which assumes that all unknownsensor nodes can detect incident signal angles from nearbysensor nodes [85, 86]. In this technique of localization, orien-tation and designing are several hops away from the beacondata. Under the noisy angle estimation assumption, this algo-rithm is intended. An evident DoA is initialized as the azi-muth direction of peak power in the case of a low-resolution transducer. A tiny array is used as an antennaand the phase derivative along the array axis is used as a mea-sure of the obvious AoA’s sine. Perhaps the obvious DoA lies

outside the precise range of arrivals. The reason can beunderstood from a situation between two array componentswith a null attenuation. The AoA capability offers nearbynodes about the node’s axis for each node bearing [87].Radial is an angle from which an object can be seen fromanother point or a reverse bearing soon. In cases wherenodes transmit their bearings concerning beacon nodes,AoA-based systems are accountable. In [88], A scheme usedto collect coincident time and AoA information at someGHz is defined. Also, the algorithm for data processing isdescribed and its outcomes are analyzed from informationcollected in two distinct structures. A model is suggestedbased on the measurement consequence used by Saleh andValenzuela (1987) in the clustered double Poisson ToAmodel. In the time-angle indoor multipath data, an accurateclustering shape was determined. The information main-tains the temporal clustering and the angle clustering shapehas also been found. Each cluster’s mean angles were deter-mined to be split evenly over all angles.

6.4. Received Signal Strength Indicator (RSSI). There aremany methods for measuring signal strength with RSSI suchas distance from the nodes and the received signals. Thesignal power obtained mostly depends on the radio wavepropagation path loss impact. If there is an obstaclebetween the sources of transmitter and receiver, the signalpower can fall considerably on the respective obstructedconnection, which is degrading the range estimate for pre-cision [89, 90]. The author has suggested an RSS-basedlocalization algorithm based onMaximum Likelihood Estima-tion (MLE) for obstructed fields with unknown Path LossExponent (PLE). An RSS-based localization was imple-mented in [91] for UWSNs using acoustic signals. They pro-posed a novel SDP estimator, including an RSS-basedtechnique and a frequency-dependent differential process,called the FDRSS-based approach, based on the UWA trans-mission loss (TL) model; these two techniques provideimportant localization effectiveness. For an underwater rangeestimation based on RSS, a fresh implementation of the Lam-bert W function is suggested. It demonstrates that thederived mathematical equation can calculate precise distanceusing four iterations in [92] using the Lambert W function.Authors predict that more UWSN applications, particularlysensor networks, will be found in the Lambert W function.The author provided an RSS-based UASN localization algo-rithm with stratification compensation (NRA-WSE) in [93].In NRAWSE, the Urick propagation model brought theRSS-based localization to quality, and the stratificationimpact is modeled by the use of the ray tracing algorithm,further adopting the Newton-Raphson algorithm for thesource localization solution. One of the fundamental prob-lems in UWSNs is to determine the location of the detectorsmostly achieved by evaluating the distance between anchornodes and unknown nodes. Ranging algorithms are generallyperformed by either assessing the ToA or RSS signal. Previ-ous studies traded underwater with the assumption ofstraight-line wave propagation with the location of sensors.However, the truth is that, due to the inhomogeneity of theunderwater framework, acoustic waves move through a

17Journal of Sensors

curved manner. The writers used an analytical relationship[94, 95] that describes the loss in transmission of acousticwaves in the inhomogeneous landscape and then on a rela-tionship basis. A unique algorithm is suggested to estimatethe variety between two sensor nodes and show that theunderwater source isotropically radiates acoustic noise anduses the RSS, which is drawn from the UWSN’s sensorsto correctly measure the source node location. Authorsdescribe another sensor model relationship between range,RSS, and the 3D Extended Kalman Filter (EKF) in [96].RF sensor is based on the underwater medium linearity ofthe RSSI value. Based on the respective sensor model, thevehicle measures the loudness of the signal and measuresthe distance to the beacon.

7. Range-Free Algorithm

To use range-free localization algorithms, we do not need touse a variety of bearing information; it only provides a coarseestimation of the node of the sensor position that is distinctfrom the range-based algorithm. A hybrid localization algo-rithm with multihop mobile underwater acoustic networkshas been suggested by the author in [97] to enhance theeffectiveness of the localization scheme in a mobile under-water medium. The sensor nodes in the network are splitinto multistage nodes for this algorithm and each stagehas a distinct localization operation. Both range-based algo-rithms and range-free algorithms are used to enhance local-ization precision and decrease communication costs.Moreover, this algorithm does not involve any previousunderstanding of the velocity of motion that is readily usedin an underwater medium. Also, the range-free algorithm iscategorized into the hop count-based, area-based algorithm,and centroid algorithms.

7.1. Hop Count-Based Algorithms. The anchor sensor nodesare placed in the hop count-based algorithm along with theboundaries or corners of a square grid. Three algorithmsare DV-Hope, solid positioning algorithm, and DHL. TheDV-Hope utilizes an average estimation of the spectrum ofhope and the counted number of hops to estimate the dis-tance to the anchor node. Robust positioning algorithm isused to raise DV-Hop by inserting an extra refinement step,while DHL may use density consciousness to dynamicallyrather than statically estimate distance.

For actual deployments where sensor node distributionis more likely in some areas, it is uneven and sparse, suchas DHL has been recommended to improve the accuracyof position estimation when the distribution of nodes inthe network is not uniform [52]. This program needs takinginto account the density of the neighborhood of the nodecalculates the average hop distance, as well as the wrongfacts distance estimates tend to accumulate with increasingpath length.

7.2. Area-Based Algorithm. Area-Based Localization Scheme(ALS) and approximate point in a triangle (APIT) are thetwo area-based algorithms. ALS is a centralized range-freesystem whose main benefits are the resistance and simplicity

in underwater to the variable sound speed. They can measurethe location of a sensor node within a specific operating areaand the sensor node clock must be synchronized in time.It presents the most recent algorithm based on ALS. Forinstance, 3D multipower area localization scheme (3D-MALS) has the function of extending 2D-ALS to 3D,whereas APIT requires a heterogeneous network. Anchorsare fitted with high-power transmitters and can accuratelyacquire location data using GPS coordinates. In [98], theauthor’s used a new technique which is based on Mel Fre-quency Cepstral Coefficients (MFCCs). The underwateracoustic method is generally nonlinear and very hard to eval-uate, so a correct nonlinear algorithm is required. Thus,MFCC is applied to underwater radiated noise extractioncharacteristics. MFCC is, therefore, an efficient recognitionand extraction algorithm.

7.3. Centroid Algorithm. The centroid algorithm is a local-ization algorithm based on proximity and coarse-grainedrange-free. The disadvantage of centroid localization algo-rithm is due to the high localization error because of the cen-troid formula, where (Xen, Yen) is the estimated location ofthe receiver.

Xen, Yen =X1 + X2 + X3+⋯Xn

n, Y1 + Y2 + Y3+⋯Yn

n

� �:

ð6Þ

For the 3D network application, the centroid algorithmwhich focuses on node self-localization may not be suitable.A distributed joint establishment control of generic multia-gent robots [99] is capable of underwater medium applica-tions such as multiautonomous surface vehicles. The ASVagent’s goal is to keep some institutions in a predefined geo-metric shape, particularly the formation of symmetric struc-tures. Furthermore, the centroid of this to-be-sustainedestablishment is to come after a denominated leader agentwhose dynamics appear like that of its other followers. Afresh 3D underwater localization algorithm utilizes threefloating buoys on the ground called anchor nodes that arefixed with GPS, RF, and acoustic transceivers. A high num-ber of nodes underwater sensors are installed at distinctdepths. These can be anchored to the bottom of the oceanand fitted on the surface of the water with floating buoys.These sensor nodes thus have restricted capacity for move-ment and are referred to as semistationary nodes.

8. Performance Evaluation of UnderwaterLocalization Schemes

In previous sections, we discussed comprehensively theunderwater localization schemes and underwater acousticnetworks. The underwater localization schemes are analyzedand compared with each other. This section contains theperformance evaluation of the abovementioned underwaterlocalization schemes concerning various aspects of locali-zation and underwater communication. The performanceevaluation is presented in Table 3.

18 Journal of Sensors

9. Challenges and Open Issues

UWSNs offer a range of applications from civil, military, andmany others. During the localization process, the detecteddata can only be interpreted usefully when responding tothe location of the sensor node, which makes localization akey problem. Thus, GPS receivers are frequently used forobtaining this in terrestrial WSNs; it is infeasible in UWSNsas less propagation of GPS signals in an underwater environ-ment. The most secure method of communication for under-water is acoustic wave communication. Underwater acousticchannels, however, are faces with low high bit error, band-width, and high delay in propagation due to severe physicallayer circumstances. Observing the current undersea acts,they are often based on complicated suppositions such astime synchronization, as they have exploited the ToA tech-nique for the most part. When AUV reaches the water sur-face, it gets its coordinates from the GPS; it dives again to acertain depth and performs dead-reckoning for underseaself-localization. The difference between LDB and AAL isthat the AUV is traveling above the operating region duringthe localization procedure and utilizes a directional acoustictransceiver to transmit its position and the angle of the trans-

ceiver beam. Synchronization might be a large challenge insuch an atmosphere. The challenges of UWSN are dividedinto two categories: first, the underwater environment, suchas the deployment of reference nodes in the deep sea, nodemobility, internode time synchronization, and signal reflec-tion owing to barriers and reflective surfaces. The second isthe underwater acoustic channel such as long delay in prop-agation, multipath fading and shadowing, sound speed vari-ation, low bit rate, heavily unreliable and asymmetric SNR,and asymmetric energy consumption. Thus, by increasingenergy consumption resultantly decreases network lifetimeand efficiency. Here, we presented some basic challengesand open issues that need to be solved in the recent future.

9.1. Time Synchronization. As discussed in Section 5, timesynchronization is the main aspect of underwater localiza-tion. The surface sensor nodes are time-synchronized byusing GPS or DNR, while the underwater sensor nodes can-not be time-synchronized, and the clocks of underwaternodes are subject to skew and offsets [60].

9.2. Reliability. Reliability is a key point in ensuring reli-ability in all forms, such as hop-by-hop, data, and end-to-

Table 3: Analysis of UASNs and underwater localization algorithms.

Localizationalgorithms

Selection methodology Advantages Drawbacks/issues

CL [41] Use a control sink.Locate nodes at both condition:postprocessing or at the end.

Required centralized center.

HL [42] Apply hydrophones/TDoA.Adopt standard oceanographic

localization issues.Range is limited.

MAS [45] Range estimates. Provide precise localization. No error estimation is performed.

3D-MASL [46] Use DET (broadcasting). Limited energy consumption. Transmission rate is variable.

ALS [46] Central sink/RSS. Reduce energy consumption. Unable to estimate the exact location.

CL [51] Automatic localization. Reduce localization errors. Valid only for limited nodes.

AAL [53] ToA/AUV. Time-synchronized. Invest more energy.

SL [57] TDoA. Required no time synchronization. Channel modeling error is not estimated.

DNR [58] GPS/acoustic. Reduce communication cost. Do not consider the sensor mobility.

LDB [59] AUV/3D deployment. Localization error estimation. Unable for 3D-free drifting UASN.

UPS/TPS [61] TDoA/extension of TWSN. Use for oil, gas, and sea exploration.Applicable only for outdoor

WSN/not for ToA.

WPS [62]Based on the premise ofsynchronized clock.

Low energy consumption andlow localization latency.

Work only in a finite region.

USP [63]Use hydrophone stations

and GPS nodes.Work in both 2D and3D environments.

Nodes reside in the overlappingarea while mapping from 2D to 3D.

AFLA [67] AUV/nodes with a particular device. No need of anchor nodes.Only depend on the neighbor nodes,no communication with anchors.

SBRAL [68] Surface water communication links. No need for LoS/ToA. Link quality is not convenient.

SLMP [36] Surface buoys and anchor nodes. Reduce communication cost. Not suitable for dynamic environment.

ToA [70] Acoustic/targets must be synchronized. Most frequently used for UASN. Time synchronization is required.

TDoA [70] Known transmission time.Do not depend on the transmission

time of source.High cost and energy consumption.

AoA [70] Based on the arrival angles.All unknown nodes can detect

incident signal angles.Ultrasound receiver increases the cost.

RSSI [70]Depend on the strength of received

signal and path loss impact.Applicable in asynchronous

scenarios.Loss caused by multipath fading.

19Journal of Sensors

end reliability. Successfully forwarding and transferring databetween participating sensor nodes in the UASN is an impor-tant aspect of reliability. Reliability guarantees successfuldelivery of packets between sensor nodes participating in col-laborative processes [100]. The review of this study foundthat reliability is the most important aspect, but unfortu-nately, most of the current studies have not considered.Therefore, it is very important to propose a cooperation algo-rithm that considers this reliability.

9.3. Node Mobility. While it is reasonable to assume thatnodes in terrestrial networks remain static, underwaternodes will inevitably drift due to underwater currents,winds, shipping activity, etc. Nodes may drift differentlyas the oceanic current is spatially dependent. While refer-ence nodes attached to surface buoys can be precisely locatedthrough GPS updates, it is difficult to maintain submergedunderwater nodes at precise locations. This may affect local-ization accuracy.

9.4. Efficiency. Efficiency is also a basic aspect in a commu-nication network to provide an efficient cooperative mech-anism and to facilitate communication among differentnodes. Based on our review of the current algorithms, it hasbeen found that no method takes into account this aspect[100, 101]. Collaborative controlling activities require anefficient method for successful data forwarding and deliveryin underwater localization. It is also required to includeefficiency in cooperative game techniques to use resourcesthat ensure efficient delivery of information, if not, thenthe cost of such information delivery will increase, i.e.,delays and throughput.

9.5. Sound Speed Variation.Most of the range-based localiza-tion techniques assume constant speed underwater sound,and it is depending on the water pressure, salinity, and tem-perature. Without measuring the sound speed, the accuracyof distance measurements based on ToA approaches maybe degraded [60]. For a fair performance comparison of alltechniques, they should be evaluated using a common andaccurate sound speed scheme.

9.6. Security and Privacy.Most of the researchers do not con-sider these two points when designing positioning algo-rithms; however, there is no doubt that they play a key rolein underwater localization. Security attacks for underwaterlocalization and countermeasures, as well as the issue of pri-vacy in underwater localization and countermeasures, arediscussed in [102]. The sensor node must display certaininformation to be localized, which can be lead to privacyholes. Location privacy is discussed in both the location-related information collection step and the estimation step.These attacks include DoS, range-based, no range estimationattacks, noncooperation, and false advertising information.

10. Conclusion

This paper presents a review of UWSNs, underwater localiza-tion, localization techniques, and the existing challenges inthe underwater environment. The paper mainly focused on

the approaches recently used in underwater localization.Localization for UWSN is an important problem that attractsconsiderable interest from scientists working on localiza-tion underwater. In this paper, the unique characteristicsof UWSN and underwater localization are explained indetail. Furthermore, the paper presented the localizationbasics, localization architecture, and the techniques used forunderwater localization. A variety of underwater localizationtechniques are discussed and compared with each otherbased on their application and efficiency as shown inTable 2. Also, a range-based and range-free localization algo-rithm is discussed which included TDoA, ToA, AoA, andRSSI. Finally, the paper presented the existing challengesand issues in underwater localization and underwater acous-tic communication. For short, it is not feasible to say that anyparticular method of localization is the best for all situationsbecause each one has certain strengths and weaknesses andconstancy for a particular situation. The ultimate goal of thisreview is to encourage and promote new scientists in theregion by offering a basis on the so far suggested underwaterlocalization. The field of USNs and localization is growingquickly, and yet many difficulties need to be investigated inthe future.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grant 61801166 and supportedin part by the Key Research and Development Program ofJiangsu under Grants BE2017071 and BE2017647. This wasalso supported by the Fundamental Research Funds for theCentral Universities under Grant 2019B22214 and in partby the Basic Science Research Program through the NationalResearch Foundation of Korea (NRF) funded by the Ministryof Education under Grant NRF-2018R1D1A1B07043331.

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